System

Abstract

An object of a system according to an embodiment is to efficiently manage the contents of an increasing number of operation mails and to clarify tasks to be executed.SOLUTION: A system includes a mail classification part, a summary creation part, and a list creation part. The mail classification unit analyzes all the received mails and classifies the contents thereof. The summary creation part summarizes the contents of each mail classified by the mail classification part. The list creation unit creates a list of items to be executed based on the contents summarized by the summary creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to efficiently manage the contents of the increasing volume of business emails and to clarify the tasks that need to be performed.

[0005] The system according to the embodiment aims to efficiently manage the contents of the increasing volume of business emails and clarify the tasks to be performed. [Means for solving the problem]

[0006] The system according to the embodiment includes a mail classification unit, a summary creation unit, and a list creation unit. The mail classification unit analyzes all received mail and classifies its contents. The summary creation unit summarizes the contents of each mail classified by the mail classification unit. The list creation unit creates a list of tasks to be performed based on the contents summarized by the summary creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the contents of the increasing volume of business emails and clarify the tasks to be performed. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The email management system according to an embodiment of the present invention uses a generation AI to create a summary of the contents of all received business emails and create a list of tasks to be performed. This allows the email management system to review the emails received each day and clarify the next actions to be taken, helping to ensure that no tasks are overlooked.

[0029] An email management system according to an embodiment includes an email classification unit, a summary creation unit, and a list creation unit. The email classification unit analyzes all received emails and classifies their contents. For example, the email classification unit uses a generation AI to classify emails into categories such as important work instructions, customer communications, reference information sharing, company-wide notices, and advertisements. The email classification unit can also use natural language processing technology to analyze the content of emails and classify them into appropriate categories. For example, the generation AI analyzes and classifies the content of emails based on prompts containing the email content. The summary creation unit summarizes the content of each email classified by the email classification unit. For example, the summary creation unit uses the generation AI to summarize an email containing important work instructions as "Submit a progress report on Project X by tomorrow." The summary creation unit can also use the generation AI to concisely summarize the content of emails. For example, the generation AI generates summaries based on prompts containing the email content. The list creation unit creates a list of tasks to be performed based on the content summarized by the summary creation unit. For example, the list creation unit uses the generation AI to add a task such as "Submit a progress report on Project X by tomorrow" to the list. The list creation unit can also use the generation AI to generate a list of tasks to be done based on a summary. For example, the generation AI generates a list of tasks to be done based on a prompt that includes a summary. This allows the email management system according to the embodiment to clearly review emails for the day and identify next actions, supporting users in ensuring that no tasks are overlooked. For example, the email management system can set reminders and notify users when a task deadline is approaching. The email management system can also manage the progress of tasks and delete completed tasks from the list. Furthermore, the email management system provides support functions to users, enabling efficient task management.

[0030] The email classification unit can estimate the sender's intention based on the sender's past behavioral history and the content of the email, enabling more accurate classification. The email classification unit, for example, uses a generation AI to analyze the email sender's past behavioral history and estimate the sender's intention in sending emails in the past. For example, the sender's intention can be identified based on the content and timing of past emails. The email classification unit can also analyze the content of emails and estimate the sender's intention. For example, the generation AI performs analysis based on prompts including the content of the email and estimates the sender's intention. This makes it possible to estimate the sender's intention and perform more accurate classification.

[0031] The email classification unit analyzes not only the content of emails but also the contents of attachments, allowing it to understand and classify the overall context of the email. For example, the email classification unit analyzes the contents of files attached to emails to understand the overall context of the email. For example, it analyzes the text and images of attachments and associates them with the email content. The email classification unit can also analyze the email content and the attachment content together. For example, the generative AI performs analysis based on prompts that include the email content, and understands the overall context of the email. This makes it possible to understand and classify the overall context of the email.

[0032] The email classification unit can classify multimedia emails collectively, including voice mails and video mails. The email classification unit, for example, analyzes the content of voice mails and video mails and classifies them in the same way as text mails. For example, it converts voice mails into text using voice recognition technology and analyzes the content. The email classification unit can also analyze the content of video mails and classify them in the same way as text mails. For example, the generation AI analyzes the content based on prompts that include the content of video mails and classifies the content. This makes it possible to classify multimedia emails collectively.

[0033] The email classification unit can automatically translate emails written in different languages ​​and classify them using unified criteria. For example, the email classification unit can automatically translate emails written in different languages ​​and classify them using unified criteria. For example, it can automatically translate emails in English, French, Chinese, etc., and analyze their contents. The email classification unit can also automatically translate emails written in different languages ​​using a machine translation algorithm. For example, the generation AI analyzes emails based on prompts containing their contents and classifies them using unified criteria. This makes it possible to classify emails written in different languages ​​using unified criteria.

[0034] When summarizing the contents of an email, the summary creation unit can refer to the summary results of past similar emails to generate a more accurate summary. When summarizing the contents of an email, the summary creation unit, for example, uses a generation AI to refer to the summary results of past similar emails. For example, it generates a summary of the current email based on the summary results of the past. The summary creation unit can also refer to the summary results of past similar emails to generate a more accurate summary. For example, the generation AI performs analysis based on a prompt that includes the content of the email and generates a summary. In this way, by referring to the summary results of past similar emails, a more accurate summary is possible.

[0035] The summary creation unit can automatically add related external information to the summary. The summary creation unit, for example, automatically adds related external information to the summary. For example, the summary creation unit incorporates the progress of a related project into the summary. The summary creation unit can also use the generation AI to add related external information to the summary. For example, the generation AI performs analysis based on a prompt that includes the summary and adds external information. In this way, the amount of information in the summary increases by automatically adding related external information.

[0036] The summary creation unit can display the summarized email content in different formats and allow the user to select. The summary creation unit, for example, displays the summarized email content in bullet points and allows the user to select. For example, important points are shown in bullet points. The summary creation unit can also display the summarized email content in a graph and allow the user to select. For example, the generation AI performs analysis based on a prompt including a summary sentence and displays it in different formats. This allows the user to select by displaying it in different formats.

[0037] The summary creation unit can automatically translate the summary into different languages, making it usable by international teams. The summary creation unit can, for example, automatically translate the summary into different languages, making it usable by international teams. For example, it can translate into English, French, Chinese, etc. The summary creation unit can also translate the summary into different languages ​​using a machine translation algorithm. For example, the generation AI performs analysis based on a prompt that includes the summary and translates it into different languages. This allows the summary to be translated into different languages, making it usable by international teams.

[0038] The list creation unit can use the generation AI to automatically set task priorities and optimize the list based on importance and urgency. The list creation unit can, for example, use the generation AI to automatically set task priorities. For example, sort tasks based on importance and urgency. The list creation unit can also use the generation AI to set task priorities and optimize the list. For example, the generation AI performs analysis based on prompts including the content of the tasks and sets priorities. This enables efficient task management by automatically setting task priorities and optimizing the list.

[0039] The list creation unit can track the progress of tasks in real time and dynamically update the list according to the progress. The list creation unit, for example, tracks the progress of tasks in real time and dynamically updates the list. For example, the list is automatically updated based on the completion status and progress of tasks. The list creation unit can also use a generation AI to track the progress of tasks and dynamically update the list. For example, the generation AI performs analysis based on prompts including the content of the task and tracks the progress. This enables efficient task management by tracking the progress of tasks in real time and dynamically updating the list.

[0040] The list creation unit can link the to-do list with other project management tools and manage it in an integrated manner. The list creation unit, for example, links the to-do list with other project management tools and manages it in an integrated manner. For example, it links with tools such as Trello and Asana to centrally manage tasks. The list creation unit can also use a generation AI to link the to-do list with other project management tools. For example, the generation AI performs analysis based on prompts including the contents of the list and links with other tools. This makes it possible to manage the to-do list in an integrated manner by linking with other project management tools.

[0041] The list creation unit can automatically assign tasks included in the list to team members so that they can be executed cooperatively. The list creation unit, for example, builds a system that automatically assigns tasks included in the list to team members. For example, the list creation unit performs assignment based on the content of the task and the skill sets of the team members. The list creation unit can also assign tasks to team members using a generation AI. For example, the generation AI performs analysis based on a prompt including the content of the task and assigns it to an appropriate member. This enables efficient task management by automatically assigning tasks to team members so that they can be executed cooperatively.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The email management system also includes a voice recognition unit that can convert the contents of voicemails into text for analysis. For example, the voice recognition unit converts the contents of voicemails into text in real time and sends it to the email classification unit. The voice recognition unit can also use generative AI to analyze the contents of voicemails and classify them into appropriate categories. This makes it possible to manage voicemails in the same way as text emails.

[0044] The email management system also includes a translation unit that can automatically translate and analyze emails written in different languages. For example, the translation unit automatically translates emails in English, French, Chinese, etc., and sends them to the email classification unit. The translation unit can also use generative AI to classify emails written in different languages ​​using unified criteria. This makes it possible to centrally manage emails written in different languages.

[0045] The email management system also has a reminder module that can notify users when a task deadline is approaching. For example, the reminder module can set a reminder based on the task deadline and notify the user. The reminder module can also use generation AI to manage the progress of tasks and delete completed tasks from the list. This makes it easier to manage task deadlines.

[0046] The email management system also includes an external information acquisition unit that can automatically add related external information to the summary. For example, the external information acquisition unit can incorporate the progress of related projects or related news articles into the summary. The external information acquisition unit can also use a generation AI to add related external information to the summary. This increases the amount of information in the summary and provides useful information to the user.

[0047] The email management system also includes a task assignment module that can automatically assign tasks included in the list to team members. For example, the task assignment module can assign tasks based on the task content and the team members' skill sets. The task assignment module can also use generative AI to assign tasks to team members. This enables efficient task management by automatically assigning tasks to team members and enabling them to work together to complete them.

[0048] The email management system also includes a project management tool integration section, which allows the to-do list to be integrated with other project management tools for integrated management. For example, the project management tool integration section can integrate with tools such as Trello and Asana to centrally manage tasks. The project management tool integration section can also use generation AI to integrate the to-do list with other project management tools. This allows for integrated management of the to-do list by integrating with other project management tools.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The email classification unit analyzes all received emails and classifies their content. For example, the email classification unit uses generative AI to classify emails into categories such as important business instructions, customer communications, reference information sharing, company-wide notices, and advertisements. The email classification unit can also use natural language processing technology to analyze the content of emails and classify them into appropriate categories. Step 2: The summary creation unit summarizes the content of each email classified by the email classification unit. For example, the summary creation unit uses the generation AI to summarize an email containing important work instructions as "Submit a progress report on project X by tomorrow." The summary creation unit can also use the generation AI to concisely summarize the content of the email. Step 3: The list creation unit creates a list of things to be done based on the content summarized by the summary creation unit. For example, the list creation unit uses generation AI to add a task such as "Submit a progress report on project X by tomorrow" to the list. The list creation unit can also use generation AI to generate a list of things to be done based on the summary.

[0051] (Example 2) The email management system according to an embodiment of the present invention uses a generation AI to create a summary of the contents of all received business emails and create a list of tasks to be performed. This allows the email management system to review the emails received each day and clarify the next actions to be taken, helping to ensure that no tasks are overlooked.

[0052] An email management system according to an embodiment includes an email classification unit, a summary creation unit, and a list creation unit. The email classification unit analyzes all received emails and classifies their contents. For example, the email classification unit uses a generation AI to classify emails into categories such as important work instructions, customer communications, reference information sharing, company-wide notices, and advertisements. The email classification unit can also use natural language processing technology to analyze the content of emails and classify them into appropriate categories. For example, the generation AI analyzes and classifies the content of emails based on prompts containing the email content. The summary creation unit summarizes the content of each email classified by the email classification unit. For example, the summary creation unit uses the generation AI to summarize an email containing important work instructions as "Submit a progress report on Project X by tomorrow." The summary creation unit can also use the generation AI to concisely summarize the content of emails. For example, the generation AI generates summaries based on prompts containing the email content. The list creation unit creates a list of tasks to be performed based on the content summarized by the summary creation unit. For example, the list creation unit uses the generation AI to add a task such as "Submit a progress report on Project X by tomorrow" to the list. The list creation unit can also use the generation AI to generate a list of tasks to be done based on a summary. For example, the generation AI generates a list of tasks to be done based on a prompt that includes a summary. This allows the email management system according to the embodiment to clearly review emails for the day and identify next actions, supporting users in ensuring that no tasks are overlooked. For example, the email management system can set reminders and notify users when a task deadline is approaching. The email management system can also manage the progress of tasks and delete completed tasks from the list. Furthermore, the email management system provides support functions to users, enabling efficient task management.

[0053] The email classification unit can estimate the sender's intention based on the sender's past behavioral history and the content of the email, enabling more accurate classification. The email classification unit, for example, uses a generation AI to analyze the email sender's past behavioral history and estimate the sender's intention in sending emails in the past. For example, the sender's intention can be identified based on the content and timing of past emails. The email classification unit can also analyze the content of emails and estimate the sender's intention. For example, the generation AI performs analysis based on prompts including the content of the email and estimates the sender's intention. This makes it possible to estimate the sender's intention and perform more accurate classification.

[0054] The email classification unit analyzes not only the content of emails but also the contents of attachments, allowing it to understand and classify the overall context of the email. For example, the email classification unit analyzes the contents of files attached to emails to understand the overall context of the email. For example, it analyzes the text and images of attachments and associates them with the email content. The email classification unit can also analyze the email content and the attachment content together. For example, the generative AI performs analysis based on prompts that include the email content, and understands the overall context of the email. This makes it possible to understand and classify the overall context of the email.

[0055] The email classification unit can use the emotion estimation function to analyze the emotional tone of an email and perform classification based on emotion. The email classification unit, for example, uses the emotion estimation function to analyze the emotional tone of an email. For example, it analyzes the context and keywords of the email and identifies positive, negative, or neutral emotions. The email classification unit can also use the emotion estimation function to analyze the emotional tone of an email and perform classification based on emotion. For example, the generation AI analyzes based on prompts including the content of the email and identifies the emotional tone. This makes it possible to perform classification based on emotion.

[0056] The email classification unit can classify multimedia emails collectively, including voice mails and video mails. The email classification unit, for example, analyzes the content of voice mails and video mails and classifies them in the same way as text mails. For example, it converts voice mails into text using voice recognition technology and analyzes the content. The email classification unit can also analyze the content of video mails and classify them in the same way as text mails. For example, the generation AI analyzes the content based on prompts that include the content of video mails and classifies the content. This makes it possible to classify multimedia emails collectively.

[0057] The email classification unit can automatically translate emails written in different languages ​​and classify them using unified criteria. For example, the email classification unit can automatically translate emails written in different languages ​​and classify them using unified criteria. For example, it can automatically translate emails in English, French, Chinese, etc., and analyze their contents. The email classification unit can also automatically translate emails written in different languages ​​using a machine translation algorithm. For example, the generation AI analyzes emails based on prompts containing their contents and classifies them using unified criteria. This makes it possible to classify emails written in different languages ​​using unified criteria.

[0058] The email classification unit can use the emotion estimation function to analyze the emotional tone of an email and provide feedback based on the user's emotions. The email classification unit, for example, uses the emotion estimation function to analyze the emotional tone of an email and provide feedback to the user. For example, an encouraging message is displayed for an email with a positive emotion. The email classification unit can also use the emotion estimation function to analyze the emotional tone of an email and provide feedback based on the user's emotions. For example, the generation AI performs analysis based on a prompt including the content of the email and provides feedback. This makes it possible to provide feedback based on the user's emotions.

[0059] When summarizing the contents of an email, the summary creation unit can refer to the summary results of past similar emails to generate a more accurate summary. When summarizing the contents of an email, the summary creation unit, for example, uses a generation AI to refer to the summary results of past similar emails. For example, it generates a summary of the current email based on the summary results of the past. The summary creation unit can also refer to the summary results of past similar emails to generate a more accurate summary. For example, the generation AI performs analysis based on a prompt that includes the content of the email and generates a summary. In this way, by referring to the summary results of past similar emails, a more accurate summary is possible.

[0060] The summary creation unit can automatically add related external information to the summary. The summary creation unit, for example, automatically adds related external information to the summary. For example, the summary creation unit incorporates the progress of a related project into the summary. The summary creation unit can also use the generation AI to add related external information to the summary. For example, the generation AI performs analysis based on a prompt that includes the summary and adds external information. In this way, the amount of information in the summary increases by automatically adding related external information.

[0061] The summary creation unit can use the emotion estimation function to generate a summary that reflects the emotional tone of the email, thereby providing a summary that takes the user's emotions into consideration. The summary creation unit, for example, uses the emotion estimation function to generate a summary that reflects the emotional tone of the email. For example, positive expressions are used for summaries of emails with positive emotions. The summary creation unit can also use the emotion estimation function to provide a summary that takes the user's emotions into consideration. For example, the generation AI performs analysis based on prompts that include the content of the email, and generates a summary that reflects the emotional tone. This makes it possible to provide a summary that takes the user's emotions into consideration by providing a summary that reflects the emotional tone.

[0062] The summary creation unit can display the summarized email content in different formats and allow the user to select. The summary creation unit, for example, displays the summarized email content in bullet points and allows the user to select. For example, important points are shown in bullet points. The summary creation unit can also display the summarized email content in a graph and allow the user to select. For example, the generation AI performs analysis based on a prompt including a summary sentence and displays it in different formats. This allows the user to select by displaying it in different formats.

[0063] The summary creation unit can automatically translate the summary into different languages, making it usable by international teams. The summary creation unit can, for example, automatically translate the summary into different languages, making it usable by international teams. For example, it can translate into English, French, Chinese, etc. The summary creation unit can also translate the summary into different languages ​​using a machine translation algorithm. For example, the generation AI performs analysis based on a prompt that includes the summary and translates it into different languages. This allows the summary to be translated into different languages, making it usable by international teams.

[0064] The summary creation unit can use the emotion estimation function to collect the user's emotional reactions to the summarized email content and improve the accuracy of the summary. The summary creation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the summarized email content. For example, it prioritizes the adoption of summary sentences with a high number of positive emotional reactions. The summary creation unit can also use the emotion estimation function to improve the accuracy of the summary based on the user's emotional reactions. For example, the generation AI performs analysis based on prompts including summary sentences and collects the user's emotional reactions. In this way, by collecting the user's emotional reactions, the accuracy of the summary can be improved.

[0065] The list creation unit can use the generation AI to automatically set task priorities and optimize the list based on importance and urgency. The list creation unit can, for example, use the generation AI to automatically set task priorities. For example, sort tasks based on importance and urgency. The list creation unit can also use the generation AI to set task priorities and optimize the list. For example, the generation AI performs analysis based on prompts including the content of the tasks and sets priorities. This enables efficient task management by automatically setting task priorities and optimizing the list.

[0066] The list creation unit can track the progress of tasks in real time and dynamically update the list according to the progress. The list creation unit, for example, tracks the progress of tasks in real time and dynamically updates the list. For example, the list is automatically updated based on the completion status and progress of tasks. The list creation unit can also use a generation AI to track the progress of tasks and dynamically update the list. For example, the generation AI performs analysis based on prompts including the content of the task and tracks the progress. This enables efficient task management by tracking the progress of tasks in real time and dynamically updating the list.

[0067] The list creation unit can use the emotion estimation function to adjust task priorities based on the user's emotional state, thereby reducing stress. The list creation unit, for example, uses the emotion estimation function to adjust task priorities based on the user's emotional state. For example, if the user is feeling stressed, it executes lower-priority tasks first. The list creation unit can also use the emotion estimation function to analyze the user's emotional state and adjust task priorities. For example, the generation AI performs analysis based on prompts that include the user's emotional state and adjusts priorities. This makes it possible to adjust task priorities based on the user's emotional state, thereby reducing stress.

[0068] The list creation unit can link the to-do list with other project management tools and manage it in an integrated manner. The list creation unit, for example, links the to-do list with other project management tools and manages it in an integrated manner. For example, it links with tools such as Trello and Asana to centrally manage tasks. The list creation unit can also use a generation AI to link the to-do list with other project management tools. For example, the generation AI performs analysis based on prompts including the contents of the list and links with other tools. This makes it possible to manage the to-do list in an integrated manner by linking with other project management tools.

[0069] The list creation unit can automatically assign tasks included in the list to team members so that they can be executed cooperatively. The list creation unit, for example, builds a system that automatically assigns tasks included in the list to team members. For example, the list creation unit performs assignment based on the content of the task and the skill sets of the team members. The list creation unit can also assign tasks to team members using a generation AI. For example, the generation AI performs analysis based on a prompt including the content of the task and assigns it to an appropriate member. This enables efficient task management by automatically assigning tasks to team members so that they can be executed cooperatively.

[0070] The list creation unit can use the emotion estimation function to monitor the user's emotional response to task progress and provide feedback to maintain motivation. The list creation unit, for example, uses the emotion estimation function to monitor the user's emotional response to task progress. For example, it provides positive feedback based on the user's emotion score. The list creation unit can also use the emotion estimation function to provide feedback based on the user's emotional response. For example, the generation AI performs analysis based on prompts including the user's emotional state and provides feedback. This enables efficient task management by monitoring the user's emotional response and providing feedback to maintain motivation.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The email management system also includes a voice recognition unit that can convert the contents of voicemails into text for analysis. For example, the voice recognition unit converts the contents of voicemails into text in real time and sends it to the email classification unit. The voice recognition unit can also use generative AI to analyze the contents of voicemails and classify them into appropriate categories. This makes it possible to manage voicemails in the same way as text emails.

[0073] The email management system also includes a translation unit that can automatically translate and analyze emails written in different languages. For example, the translation unit automatically translates emails in English, French, Chinese, etc., and sends them to the email classification unit. The translation unit can also use generative AI to classify emails written in different languages ​​using unified criteria. This makes it possible to centrally manage emails written in different languages.

[0074] The email management system can further use an emotion estimation function to analyze the emotional tone of an email and provide feedback based on the user's emotions. For example, the emotion estimation function can analyze the context and keywords of an email to identify positive, negative, or neutral emotions. The emotion estimation function can also display messages of encouragement or advice based on the user's emotions. This enables feedback that takes the user's emotions into consideration.

[0075] The email management system can further use the emotion estimation function to generate summaries that reflect the emotional tone of the email, thereby providing summaries that take the user's emotions into consideration. For example, the emotion estimation function uses positive expressions in summaries of emails with positive emotions. The emotion estimation function can also use cautionary expressions in summaries of emails with negative emotions. This makes it possible to provide summaries that reflect the emotional tone.

[0076] The email management system can further use the emotion estimation function to adjust task priorities based on the user's emotional state, thereby reducing stress. For example, if the user is feeling stressed, the emotion estimation function can execute lower priority tasks first. The emotion estimation function can also analyze the user's emotional state and adjust task priorities. This makes it possible to adjust task priorities based on the user's emotional state, thereby reducing stress.

[0077] The email management system also has a reminder module that can notify users when a task deadline is approaching. For example, the reminder module can set a reminder based on the task deadline and notify the user. The reminder module can also use generation AI to manage the progress of tasks and delete completed tasks from the list. This makes it easier to manage task deadlines.

[0078] The email management system also includes an external information acquisition unit that can automatically add related external information to the summary. For example, the external information acquisition unit can incorporate the progress of related projects or related news articles into the summary. The external information acquisition unit can also use a generation AI to add related external information to the summary. This increases the amount of information in the summary and provides useful information to the user.

[0079] The email management system also includes a task assignment module that can automatically assign tasks included in the list to team members. For example, the task assignment module can assign tasks based on the task content and the team members' skill sets. The task assignment module can also use generative AI to assign tasks to team members. This enables efficient task management by automatically assigning tasks to team members and enabling them to work together to complete them.

[0080] The email management system can further use the emotion estimation function to monitor the user's emotional response to task progress and provide feedback to maintain motivation. For example, the emotion estimation function can provide positive feedback based on the user's emotion score. The emotion estimation function can also provide feedback based on the user's emotional response. This makes it possible to monitor the user's emotional response and provide feedback to maintain motivation.

[0081] The email management system also includes a project management tool integration section, which allows the to-do list to be integrated with other project management tools for integrated management. For example, the project management tool integration section can integrate with tools such as Trello and Asana to centrally manage tasks. The project management tool integration section can also use generation AI to integrate the to-do list with other project management tools. This allows for integrated management of the to-do list by integrating with other project management tools.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The email classification unit analyzes all received emails and classifies their content. For example, the email classification unit uses generative AI to classify emails into categories such as important business instructions, customer communications, reference information sharing, company-wide notices, and advertisements. The email classification unit can also use natural language processing technology to analyze the content of emails and classify them into appropriate categories. Step 2: The summary creation unit summarizes the content of each email classified by the email classification unit. For example, the summary creation unit uses the generation AI to summarize an email containing important work instructions as "Submit a progress report on project X by tomorrow." The summary creation unit can also use the generation AI to concisely summarize the content of the email. Step 3: The list creation unit creates a list of things to be done based on the content summarized by the summary creation unit. For example, the list creation unit uses generation AI to add a task such as "Submit a progress report on project X by tomorrow" to the list. The list creation unit can also use generation AI to generate a list of things to be done based on the summary.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an email classification unit that analyzes all received emails and classifies their contents; a summary creation unit that summarizes the content of each email classified by the email classification unit; a list creation unit that creates a list of things to be done based on the content summarized by the summary creation unit. A system characterized by:

2. The email classification unit Analyzes not only the content of the email but also the contents of the attachments, understands the overall context of the email, and classifies it.

2. The system of claim 1.

3. The email classification unit Categorize all multimedia emails together, including voice or video emails 2. The system of claim 1.

4. The summary creation unit When summarizing the content of the email, the system references the summaries of similar emails in the past to generate a more accurate summary.

2. The system of claim 1.

5. The list creation unit Use generative AI to automatically prioritize tasks and optimize the list based on importance and urgency 2. The system of claim 1.

6. The email classification unit Analyze the emotional tone of the email and classify it based on emotion 2. The system of claim 1.

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